Public Member Functions | Public Attributes | Private Attributes | List of all members
calibration_estimation.opt_runner.ErrorCalc Class Reference

Public Member Functions

def __init__ (self, robot_params, free_dict, multisensors, use_cov)
 
def calculate_error (self, opt_all_vec)
 
def calculate_full_param_vec (self, opt_param_vec)
 
def calculate_jacobian (self, opt_all_vec)
 
def multisensor_pose_jacobian (self, opt_param_vec, pose_param_vec, multisensor)
 
def single_sensor_params_jacobian (self, opt_param_vec, target_pose_T, target_id, sensor)
 
def split_all (self, opt_all_vec)
 

Public Attributes

 marker_pub
 

Private Attributes

 _expanded_params
 
 _free_list
 
 _j_count
 
 _multisensors
 
 _robot_params
 
 _use_cov
 

Detailed Description

Helpers for computing errors and jacobians

Definition at line 47 of file opt_runner.py.

Constructor & Destructor Documentation

def calibration_estimation.opt_runner.ErrorCalc.__init__ (   self,
  robot_params,
  free_dict,
  multisensors,
  use_cov 
)

Definition at line 51 of file opt_runner.py.

Member Function Documentation

def calibration_estimation.opt_runner.ErrorCalc.calculate_error (   self,
  opt_all_vec 
)

Definition at line 70 of file opt_runner.py.

def calibration_estimation.opt_runner.ErrorCalc.calculate_full_param_vec (   self,
  opt_param_vec 
)
Take the set of optimization params, and expand it into the bigger param vec

Definition at line 61 of file opt_runner.py.

def calibration_estimation.opt_runner.ErrorCalc.calculate_jacobian (   self,
  opt_all_vec 
)
Full Jacobian:
  The resulting returned jacobian can be thought of as a set of several concatenated jacobians as follows:

[ J_multisensor_0 ]
    J = [ J_multisensor_1 ]
[        :        ]
[ J_multisensor_M ]

  where M is the number of multisensors, which is generally the number of calibration samples collected.

Multisensor Jacobian:
  The Jacobian for a given multisensor is created by concatenating jacobians for each sensor

            [ J_sensor_m_0 ]
  J_multisensor_m = [ J_sensor_m_1 ]
            [      :       ]
            [ J_sensor_m_S ]

  where S is the number of sensors in this multisensor.

Sensor Jacobian:
  A single sensor jacobian is created by concatenating jacobians for the system parameters and checkerboards

  J_sensor_m_s = [ J_params_m_s | J_m_s_pose0 | J_m_s_pose1 | --- | J_m_s_CB_poseC ]

  The number of rows in J_sensor_m_s is equal to the number of rows in this sensor's residual.
  J_params_m_s shows how modifying the free system parameters affects the residual.
  Each J_m_s_pose_c is 6 columns wide and shows how moving a certain target affects the residual. All
    of the J_m_s_pose blocks are zero, except J_sensor_pose_m, since target m is the only target that
    was viewed by the sensors in this multisensor.

Definition at line 146 of file opt_runner.py.

def calibration_estimation.opt_runner.ErrorCalc.multisensor_pose_jacobian (   self,
  opt_param_vec,
  pose_param_vec,
  multisensor 
)
Generates the jacobian from a target pose to the multisensor's residual.

Input:
- opt_param_vec: Vector of the free system parameters (the parameters we're actuall optimizing)
- pose_param_vec: Vector of length 6 encoding the target's pose 0:3=translation 3:6=rotation_axis
- multisensor: The actual multisensor definition.
Output:
- J_scaled: An mx6 jacobian, where m is the length of multisensor's residual. There are 6 columns since the
    target's pose is encoded using 6 parameters.
    If covariance calculations are enabled, then the Jacobian is scaled by sqrt(Gamma), where Gamma
    is the information matrix for this measurement.

Definition at line 294 of file opt_runner.py.

def calibration_estimation.opt_runner.ErrorCalc.single_sensor_params_jacobian (   self,
  opt_param_vec,
  target_pose_T,
  target_id,
  sensor 
)
Computes the jacobian from the free system parameters to a single sensor
Inputs:
- opt_param_vec: Vector of the free system parameters (the parameters we're actuall optimizing)
- target_pose_T: The pose of the target that this sensor is viewing
- target_id: String name of the target that this sensor is viewing. This is necessary to generate the
     set of feature points on the target
- sensor: The actual sensor definition
Output:
- J_scaled: An mxn jacobian, where m is the length of sensor's residual and n is the length of opt_param_vec.
    If covariance calculations are enabled, then the Jacobian is scaled by sqrt(Gamma), where Gamma
    is the information matrix for this measurement.

Definition at line 237 of file opt_runner.py.

def calibration_estimation.opt_runner.ErrorCalc.split_all (   self,
  opt_all_vec 
)
Splits the input vector into two parts:
  1) opt_param_vec: The system parameters that we're optimizing,
  2) full_pose_arr: A Nx6 array where each row is a checkerboard pose

Definition at line 224 of file opt_runner.py.

Member Data Documentation

calibration_estimation.opt_runner.ErrorCalc._expanded_params
private

Definition at line 53 of file opt_runner.py.

calibration_estimation.opt_runner.ErrorCalc._free_list
private

Definition at line 54 of file opt_runner.py.

calibration_estimation.opt_runner.ErrorCalc._j_count
private

Definition at line 57 of file opt_runner.py.

calibration_estimation.opt_runner.ErrorCalc._multisensors
private

Definition at line 55 of file opt_runner.py.

calibration_estimation.opt_runner.ErrorCalc._robot_params
private

Definition at line 52 of file opt_runner.py.

calibration_estimation.opt_runner.ErrorCalc._use_cov
private

Definition at line 56 of file opt_runner.py.

calibration_estimation.opt_runner.ErrorCalc.marker_pub

Definition at line 58 of file opt_runner.py.


The documentation for this class was generated from the following file:


calibration_estimation
Author(s): Vijay Pradeep, Michael Ferguson
autogenerated on Thu Jun 6 2019 19:17:16